AMD has announced the Radeon Vega Frontier Edition, a high-end GPU based on a new architecture (Vega 1) which will launch in June.
Unlike some other recent AMD GPUs such as the Radeon Fury X, the Radeon Vega card has half precision compute capability (FP16 operations) that is twice as fast as single precision compute. AMD is advertising 13 TFLOPS single precision, 26 TFLOPS double precision for the Radeon Vega Frontier Edition.
The GPU includes 16 GB of High Bandwidth Memory 2.0 VRAM. The per-pin memory clock is up to around 1.88 Gbps, but total memory bandwidth is slightly lower than the Radeon Fury X, due to the memory bus being cut to 2048-bit from 4096-bit. However, the Fury X included only 4 GB of HBM1. The new card could include four stacks with 4 GB each, or it could be the first product to include 8 GB stacks of High Bandwidth Memory, a capacity which has not been sold by Samsung or SK Hynix to date.
The new GPU is aimed at professional/workstation users rather than gamers:
As important as the Vega hardware itself is, for AMD the target market for the hardware is equally important if not more. Vega's the first new high-end GPU from the company in two years, and it comes at a time when GPU sales are booming. Advances in machine learning have made GPUs the hottest computational peripheral since the x87 floating point co-processor, and unfortunately for AMD, they've largely missed the boat on this. Competitor NVIDIA has vastly grown their datacenter business over just the last year on the back of machine learning, thanks in large part to the task-optimized capabilities of the Pascal architecture. And most importantly of all, these machine learning accelerators have been highly profitable, fetching high margins even when the cards are readily available.
For AMD then, Vega is their chance to finally break into the machine learning market in a big way. The GPU isn't just a high-end competitor, but it offers high performance FP16 and INT8 modes that earlier AMD GPU architectures lacked, and those modes are in turn immensely beneficial to machine learning performance. As a result, for the Vega Frontier Edition launch, AMD is taking a page from the NVIDIA playbook: rather than starting off the Vega generation with consumer cards, they're going to launch with professional cards for the workstation market.
To be sure, the Radeon Vega Frontier Edition is not officially branded as a Pro or WX series card. But in terms of AMD's target market, it's unambiguously a professional card. The product page is hosted on the pro graphics section of AMD's website, the marketing material is all about professional uses, and AMD even goes so far as to tell gamers to hold off for cheaper gaming cards later on in their official blog post. Consequently the Vega FE is about the closest analogue AMD has to NVIDIA's Titan series cards, which although are gaming capable, in the last generation they have become almost exclusively professional focused.
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First it was unveiled, now it has launched. AMD has launched the Radeon Vega Frontier Edition at $999 for the air-cooled version and $1499 for liquid-cooled. The High Bandwidth Memory 2.0 included has been confirmed to be two stacks of 8-layer 8 GB HBM:
After what appears to be a very unusual false start, AMD has now formally launched their new Radeon Vega Frontier Edition card. First announced back in mid-May, the unusual card, which AMD is all but going out of their way to dissuade their usual consumer base from buying, will be available today for $999. Meanwhile its liquid cooled counterpart, which was also announced at the time, will be available later on in Q3 for $1499.
Interestingly, both of these official prices are some $200-$300 below the prices first listed by SabrePC two weeks ago in the false start. To date AMD hasn't commented on what happened there, however it's worth noting that SabrePC is as of press time still listing the cards for their previous prices, with both cards reporting as being in-stock.
[...] Feeding the GPU is AMD's previously announced dual stack HBM2 configuration, which is now confirmed to be a pair of 8 layer, 8GB "8-Hi" stacks. AMD has the Vega FE's memory clocked at just under 1.9Gbps, which gives the card a total memory bandwidth of 483GB/sec. And for anyone paying close attention to AMD's naming scheme here, they are officially calling this "HBC" memory – a callback to Vega's High Bandwidth Cache design.
AMD has announced two new GPUs, the Radeon RX Vega 64 and 56. The GPUs are named in reference to the amount of "compute units" included. Both GPUs have 8 GB of High Bandwidth Memory 2.0 VRAM and will be released on August 14.
The Vega 64 is priced at $500 and is said to be on par with Nvidia's GeForce GTX 1080. The GTX 1080 was released on May 27, 2016 and has a TDP 105 Watts lower than the Vega 64.
Previously: AMD Unveils the Radeon Vega Frontier Edition
AMD Launches the Radeon Vega Frontier Edition
AMD's new Vega 64 GPU offers comparable performance at a similar price to Nvidia's GTX 1080, which was released over a year ago. But it does so while consuming a lot more power under load (over 100 Watts more). Vega 56, however, runs faster than the GTX 1070 at a slightly lower price:
So how does AMD fare? The answer to that is ultimately going to hinge on your option on power efficiency. But before we get too far, let's start with the Radeon RX Vega 64, AMD's flagship card. Previously we've been told that it would trade blows with NVIDIA's GeForce GTX 1080, and indeed it does just that. At 3840x2160, the Vega 64 is on average neck-and-neck with the GeForce GTX 1080 in gaming performance, with the two cards routinely trading the lead, and AMD holding it more often. Of course the "anything but identical" principle applies here, as while the cards are equal on average, they can sometimes be quite far apart on individual games.
Unfortunately for AMD, their GTX 1080-like performance doesn't come cheap from a power perspective. The Vega 64 has a board power rating of 295W, and it lives up to that rating. Relative to the GeForce GTX 1080, we've seen power measurements at the wall anywhere between 110W and 150W higher than the GeForce GTX 1080, all for the same performance. Thankfully for AMD, buyers are focused on price and performance first and foremost (and in that order), so if all you're looking for is a fast AMD card at a reasonable price, the Vega 64 delivers where it needs to: it is a solid AMD counterpart to the GeForce GTX 1080. However if you care about the power consumption and the heat generated by your GPU, the Vega 64 is in a very rough spot.
On the other hand, the Radeon RX Vega 56 looks better for AMD, so it's easy to see why in recent days they have shifted their promotional efforts to the cheaper member of the RX Vega family. Though a step down from the RX Vega 64, the Vega 56 delivers around 90% of Vega 64's performance for 80% of the price. Furthermore, when compared head-to-head with the GeForce GTX 1070, its closest competition, the Vega 56 enjoys a small but none the less significant 8% performance advantage over its NVIDIA counterpart. Whereas the Vega 64 could only draw to a tie, the Vega 56 can win in its market segment.
[...] The one wildcard here with the RX Vega 56 is going to be where retail prices actually end up. AMD's $399 MSRP is rather aggressive, especially when GTX 1070 cards are retailing for closer to $449 due to cryptocurrency miner demand. If they can sustain that price, then Vega 56 is going to be real hot stuff, besting GTX 1070 in price and performance. Otherwise at GTX 1070-like prices it still has the performance advantage, but not the initiative on pricing. At any rate, this is a question we can't answer today; the Vega 56 won't be launching for another two weeks.
Both the Vega 64 and Vega 56 include 8 GB of HBM2 memory.
Also at Tom's Hardware.
Previously: AMD Unveils the Radeon Vega Frontier Edition
AMD Launches the Radeon Vega Frontier Edition
AMD Radeon RX Vega 64 and 56 Announced
(Score: -1, Spam) by Anonymous Coward on Thursday May 18 2017, @07:23PM
mmmnnnnnnn, mmmmnnn!
what I would do for some raw ostrich pussy!
go on, flap those wings against me, OH YEAH!
(Score: 2) by DannyB on Thursday May 18 2017, @09:06PM (1 child)
It seems that every year the silicon gets more and more horsepower*. Clock rates don't seem to be climbing. But we get more done per clock cycle, and with more functional units, cores and "psuedo cores". For ten years I have believed that core count would rise more rapidly than it has. Especially since languages and programming techniques have begun to adapt to the concept of using more processing elements. Isolate what work units can be done on a single processing element, independent of other processing elements. Does it work on 8 cores? What about on 1000 cores?
Then there are GPUs. Impressive beasts. But I do not use GPUs for graphics, and have never been a gamer**. It takes effort to exploit the hardware in a GPU. I've recently begun dabbling with OpenCL, and writing kernel in C. I've long wondered about the possibility of having more but simpler cores. What if I could have, say 64 cores, that were about the complexity of a simple variation of the ARM instruction set, for example, but programmed in a much more conventional way than a GPU. This would seem to be vastly more useful than a GPU for non-graphics parallel execution. They wouldn't necessarily even need to have shared memory as long as there was reasonably fast communication. I would still have to organize my problem into independent units. But it wouldn't be single instruction multiple data. I could hand off work units to processing elements. Work units may turn out to complete in different amounts of time. But with a queue, work units (eg, the input data to the same code) could be fed to the next available processing element. Suppose each work item were processing pixels, then if you pick a suitable sized block of pixels for each work unit, then you could keep each processing element busy for a couple of seconds.
Just to dream, an interactive mandelbrot program that can deep dive using multi-precision arithmetic because a double is just not enough. And I know how to do it with only integers. Doing multi precision, eg multi word arithmetic in an Open CL kernel will be a trick. At least at my present naive level of OpenCL experience.
If there were enough conventional general purpose cores, then would the need for GPUs, as they are today, eventually go away? In other words, would hardware dedicated to a special purpose be needed if there were enough parallel general purpose silicon with many cores? How much easier would graphics programming be? How much easier would this be to program things other than graphics? How many more real world problems might be solved using such hardware if it were ever widely available? (Of course, once upon a time it was a dream to ask what if microprocessors could run at 100 MHz.)
* cpu horsepower = the amount of thinking that can be done by one horse in one day
** A long time ago in a slashdot far, far away I once had a sig: "gamers are the root of all evil". Because gamers used games as an excuse to run Windows.
To transfer files: right-click on file, pick Copy. Unplug mouse, plug mouse into other computer. Right-click, paste.
(Score: 2) by Snotnose on Thursday May 18 2017, @11:19PM
For ten years I have believed that core count would rise more rapidly than it has.
Parallel processing is *hard*. In the mid 80s my company slightly modified our existing product so you could slide more CPUs into it (think S-100 bus tech). If memory serves we could get 8 CPU cards in the unit, or fewer than 8 and dedicated cards (system had 8 slots and a bunch of cards you could plug into it).
We actually had some version of Unix running the whole shebang, but the whole balancing act of keeping all CPUs fully occupied without swamping the bus was a bitch. Ian, the guy in charge, was a PhD candidate at UCSD at the time, hella smart guy. Ended up moving to the bay area and getting richer than rich.
The product did fairly well, not spectacular.
/ MIMD - multiple instruction multiple data - a real bitch. All CPUs running different code on different data
// SIMD - Single instruction multiple data - think graphics card
/// MISD - Multiple instruction Single data - What we did, missile telemetry stream interpreted multiple ways
//// MIMD - Multiple instruction, multiple data. We did not do this
When the dust settled America realized it was saved by a porn star.
(Score: 2) by opinionated_science on Friday May 19 2017, @11:52AM
I think it may be 13TF single, 26 half-precision and 7 or so double...